Understanding the structure and dynamics of
networks are of vital importance to winning the global war on
terror. To fully comprehend the network environment, analysts
must be able to investigate interconnected relationships of many
diverse network types simultaneously as they evolve both
spatially and temporally. To remove the burden from the analyst
of making mental correlations of observations and conclusions
from multiple domains, we introduce the Dynamic Graph
Analytic Framework (DYGRAF). DYGRAF provides the
infrastructure which facilitates a layered multi-modal network
analysis (LMMNA) approach that enables analysts to assemble
previously disconnected, yet related, networks in a common
battle space picture. In doing so, DYGRAF provides the analyst
with timely situation awareness, understanding and anticipation
of threats, and support for effective decision-making in diverse
environments.
KEYWORDS: Situational awareness sensors, Information fusion, Environmental sensing, Data modeling, Reliability, Algorithm development, Sensors, Chemical elements, Chemical species, Analytical research
Information Fusion Engine for Real-time Decision Making (INFERD) is a tool that was developed to supplement current graph matching techniques in Information Fusion models. Based on sensory data and a priori models, INFERD dynamically generates, evolves, and evaluates hypothesis on the current state of the environment. The a priori models developed are hierarchical in nature lending them to a multi-level Information Fusion process whose primary output provides a situational awareness of the environment of interest in the context of the models running. In this paper we look at INFERD's multi-level fusion approach and provide insight on the inherent problems such as fragmentation in the approach and the research being undertaken to mitigate those deficiencies. Due to the large variance of data in disparate environments, the awareness of situations in those environments can be drastically different. To accommodate this, the INFERD framework provides support for plug-and-play fusion modules which can be developed specifically for domains of interest. However, because the models running in INFERD are graph based, some default measurements can be provided and will be discussed in the paper. Among these are a Depth measurement to determine how much danger is presented by the action taking place, a Breadth measurement to gain information regarding the scale of an attack that is currently happening, and finally a Reliability measure to tell the user the credibility of a particular hypothesis. All of these results will be demonstrated in the Cyber domain where recent research has shown to be an area that is welldefined and bounded, so that new models and algorithms can be developed and evaluated.
Conference Committee Involvement (1)
Intelligent Sensing, Situation Management, and Impact Assessment
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